Beyond Waveform Robustness: Robust Feature-Vocoder Adversarial Attacks on Automatic Speech Recognition
arXiv:2606.05678v1 Announce Type: cross Abstract: Automatic speech recognition (ASR) systems have become widely used for multilingual speech-to-text transcription. Their robustness to adversarial attacks has become an important topic for the community. Existing adversarial attacks directly add adversarial noise to the speech audio. However, prior work has shown that existing adversarial attacks face two limitations: they often transfer poorly to black-box ASR systems and are increasingly mitigated by defenses tailored to input-space perturbations. In this work, we propose a Clean-Referenced Fe
The proliferation of ASR systems across critical applications necessitates robust security, making research into advanced adversarial attacks and defenses timely.
This research reveals a new vector for challenging the security and reliability of ASR systems, pushing the boundaries of adversarial machine learning and requiring more sophisticated defense strategies.
The focus of adversarial attacks shifts from direct waveform manipulation to feature-vocoder based methods, posing new challenges for black-box ASR systems and existing mitigation techniques.
- · Adversarial ML researchers
- · Cybersecurity firms specializing in AI
- · Hardware-based security for AI
- · ASR system developers without robust defenses
- · Current input-space perturbation defenses
- · Industries heavily reliant on unhardened ASR
ASR systems will require more advanced, potentially hardware-level, security measures to counter these new attack vectors.
Increased research and development into multimodal or semantic-level defenses may emerge to protect against feature-space attacks.
The perceived trustworthiness of AI systems in sensitive applications could degrade if robust defenses fail to keep pace with attack sophistication.
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Read at arXiv cs.AI